16,718 research outputs found
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
MonALISA : A Distributed Monitoring Service Architecture
The MonALISA (Monitoring Agents in A Large Integrated Services Architecture)
system provides a distributed monitoring service. MonALISA is based on a
scalable Dynamic Distributed Services Architecture which is designed to meet
the needs of physics collaborations for monitoring global Grid systems, and is
implemented using JINI/JAVA and WSDL/SOAP technologies. The scalability of the
system derives from the use of multithreaded Station Servers to host a variety
of loosely coupled self-describing dynamic services, the ability of each
service to register itself and then to be discovered and used by any other
services, or clients that require such information, and the ability of all
services and clients subscribing to a set of events (state changes) in the
system to be notified automatically. The framework integrates several existing
monitoring tools and procedures to collect parameters describing computational
nodes, applications and network performance. It has built-in SNMP support and
network-performance monitoring algorithms that enable it to monitor end-to-end
network performance as well as the performance and state of site facilities in
a Grid. MonALISA is currently running around the clock on the US CMS test Grid
as well as an increasing number of other sites. It is also being used to
monitor the performance and optimize the interconnections among the reflectors
in the VRVS system.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 8 pages, pdf. PSN MOET00
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